Overview

Dataset statistics

Number of variables21
Number of observations2333
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory449.6 KiB
Average record size in memory197.3 B

Variable types

Categorical3
Numeric15
Boolean3

Alerts

state has a high cardinality: 51 distinct valuesHigh cardinality
phone number has a high cardinality: 2333 distinct valuesHigh cardinality
number vmail messages is highly overall correlated with voice mail planHigh correlation
total day minutes is highly overall correlated with total day chargeHigh correlation
total day charge is highly overall correlated with total day minutesHigh correlation
total eve minutes is highly overall correlated with total eve chargeHigh correlation
total eve charge is highly overall correlated with total eve minutesHigh correlation
total night minutes is highly overall correlated with total night chargeHigh correlation
total night charge is highly overall correlated with total night minutesHigh correlation
total intl minutes is highly overall correlated with total intl chargeHigh correlation
total intl charge is highly overall correlated with total intl minutesHigh correlation
voice mail plan is highly overall correlated with number vmail messagesHigh correlation
international plan is highly imbalanced (54.0%)Imbalance
phone number is uniformly distributedUniform
phone number has unique valuesUnique
number vmail messages has 1695 (72.7%) zerosZeros
customer service calls has 508 (21.8%) zerosZeros

Reproduction

Analysis started2023-03-02 03:19:13.765341
Analysis finished2023-03-02 03:19:55.572504
Duration41.81 seconds
Software versionpandas-profiling vdev
Download configurationconfig.json

Variables

state
Categorical

Distinct51
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size36.5 KiB
WV
 
70
NY
 
64
OR
 
59
TX
 
59
OH
 
58
Other values (46)
2023 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters4666
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNE
2nd rowWI
3rd rowNJ
4th rowNV
5th rowHI

Common Values

ValueCountFrequency (%)
WV 70
 
3.0%
NY 64
 
2.7%
OR 59
 
2.5%
TX 59
 
2.5%
OH 58
 
2.5%
MN 58
 
2.5%
MI 57
 
2.4%
CT 54
 
2.3%
MT 54
 
2.3%
WY 54
 
2.3%
Other values (41) 1746
74.8%

Length

2023-03-01T19:19:55.645310image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wv 70
 
3.0%
ny 64
 
2.7%
or 59
 
2.5%
tx 59
 
2.5%
oh 58
 
2.5%
mn 58
 
2.5%
mi 57
 
2.4%
ct 54
 
2.3%
mt 54
 
2.3%
wy 54
 
2.3%
Other values (41) 1746
74.8%

Most occurring characters

ValueCountFrequency (%)
N 504
 
10.8%
A 473
 
10.1%
M 447
 
9.6%
I 354
 
7.6%
T 304
 
6.5%
D 274
 
5.9%
O 244
 
5.2%
C 243
 
5.2%
W 212
 
4.5%
V 203
 
4.4%
Other values (14) 1408
30.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4666
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 504
 
10.8%
A 473
 
10.1%
M 447
 
9.6%
I 354
 
7.6%
T 304
 
6.5%
D 274
 
5.9%
O 244
 
5.2%
C 243
 
5.2%
W 212
 
4.5%
V 203
 
4.4%
Other values (14) 1408
30.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 4666
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 504
 
10.8%
A 473
 
10.1%
M 447
 
9.6%
I 354
 
7.6%
T 304
 
6.5%
D 274
 
5.9%
O 244
 
5.2%
C 243
 
5.2%
W 212
 
4.5%
V 203
 
4.4%
Other values (14) 1408
30.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4666
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 504
 
10.8%
A 473
 
10.1%
M 447
 
9.6%
I 354
 
7.6%
T 304
 
6.5%
D 274
 
5.9%
O 244
 
5.2%
C 243
 
5.2%
W 212
 
4.5%
V 203
 
4.4%
Other values (14) 1408
30.2%

account length
Real number (ℝ)

Distinct205
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.43463
Minimum1
Maximum243
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.5 KiB
2023-03-01T19:19:55.780992image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile34.6
Q173
median100
Q3127
95-th percentile165
Maximum243
Range242
Interquartile range (IQR)54

Descriptive statistics

Standard deviation39.64247
Coefficient of variation (CV)0.39470916
Kurtosis-0.065715643
Mean100.43463
Median Absolute Deviation (MAD)27
Skewness0.088515393
Sum234314
Variance1571.5254
MonotonicityNot monotonic
2023-03-01T19:19:55.940694image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93 33
 
1.4%
105 32
 
1.4%
112 30
 
1.3%
116 30
 
1.3%
87 29
 
1.2%
90 28
 
1.2%
100 28
 
1.2%
99 28
 
1.2%
101 26
 
1.1%
64 26
 
1.1%
Other values (195) 2043
87.6%
ValueCountFrequency (%)
1 5
0.2%
2 1
 
< 0.1%
3 3
0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 2
 
0.1%
8 1
 
< 0.1%
9 3
0.1%
10 2
 
0.1%
11 3
0.1%
ValueCountFrequency (%)
243 1
< 0.1%
232 1
< 0.1%
225 2
0.1%
224 2
0.1%
221 1
< 0.1%
217 1
< 0.1%
215 1
< 0.1%
210 2
0.1%
209 2
0.1%
208 1
< 0.1%

area code
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size36.5 KiB
415
1178 
408
588 
510
567 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6999
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row415
2nd row510
3rd row415
4th row510
5th row510

Common Values

ValueCountFrequency (%)
415 1178
50.5%
408 588
25.2%
510 567
24.3%

Length

2023-03-01T19:19:56.073341image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-01T19:19:56.195011image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
415 1178
50.5%
408 588
25.2%
510 567
24.3%

Most occurring characters

ValueCountFrequency (%)
4 1766
25.2%
1 1745
24.9%
5 1745
24.9%
0 1155
16.5%
8 588
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6999
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 1766
25.2%
1 1745
24.9%
5 1745
24.9%
0 1155
16.5%
8 588
 
8.4%

Most occurring scripts

ValueCountFrequency (%)
Common 6999
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 1766
25.2%
1 1745
24.9%
5 1745
24.9%
0 1155
16.5%
8 588
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6999
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 1766
25.2%
1 1745
24.9%
5 1745
24.9%
0 1155
16.5%
8 588
 
8.4%

phone number
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct2333
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size36.5 KiB
421-8535
 
1
330-5824
 
1
340-4972
 
1
378-9506
 
1
383-5976
 
1
Other values (2328)
2328 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters18664
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2333 ?
Unique (%)100.0%

Sample

1st row421-8535
2nd row417-2265
3rd row327-9341
4th row419-9688
5th row364-8128

Common Values

ValueCountFrequency (%)
421-8535 1
 
< 0.1%
330-5824 1
 
< 0.1%
340-4972 1
 
< 0.1%
378-9506 1
 
< 0.1%
383-5976 1
 
< 0.1%
334-4438 1
 
< 0.1%
393-4823 1
 
< 0.1%
365-5682 1
 
< 0.1%
409-8814 1
 
< 0.1%
366-4444 1
 
< 0.1%
Other values (2323) 2323
99.6%

Length

2023-03-01T19:19:56.297736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
421-8535 1
 
< 0.1%
338-6556 1
 
< 0.1%
408-4836 1
 
< 0.1%
327-9341 1
 
< 0.1%
419-9688 1
 
< 0.1%
364-8128 1
 
< 0.1%
338-9210 1
 
< 0.1%
353-2630 1
 
< 0.1%
409-3786 1
 
< 0.1%
392-2733 1
 
< 0.1%
Other values (2323) 2323
99.6%

Most occurring characters

ValueCountFrequency (%)
3 3268
17.5%
- 2333
12.5%
4 1967
10.5%
5 1449
7.8%
7 1432
7.7%
6 1432
7.7%
9 1418
7.6%
1 1393
7.5%
8 1390
7.4%
2 1348
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16331
87.5%
Dash Punctuation 2333
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 3268
20.0%
4 1967
12.0%
5 1449
8.9%
7 1432
8.8%
6 1432
8.8%
9 1418
8.7%
1 1393
8.5%
8 1390
8.5%
2 1348
8.3%
0 1234
 
7.6%
Dash Punctuation
ValueCountFrequency (%)
- 2333
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18664
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 3268
17.5%
- 2333
12.5%
4 1967
10.5%
5 1449
7.8%
7 1432
7.7%
6 1432
7.7%
9 1418
7.6%
1 1393
7.5%
8 1390
7.4%
2 1348
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18664
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 3268
17.5%
- 2333
12.5%
4 1967
10.5%
5 1449
7.8%
7 1432
7.7%
6 1432
7.7%
9 1418
7.6%
1 1393
7.5%
8 1390
7.4%
2 1348
7.2%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.5 KiB
False
2106 
True
227 
ValueCountFrequency (%)
False 2106
90.3%
True 227
 
9.7%
2023-03-01T19:19:56.408568image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.5 KiB
False
1695 
True
638 
ValueCountFrequency (%)
False 1695
72.7%
True 638
 
27.3%
2023-03-01T19:19:56.508264image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

number vmail messages
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct45
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0282898
Minimum0
Maximum51
Zeros1695
Zeros (%)72.7%
Negative0
Negative (%)0.0%
Memory size36.5 KiB
2023-03-01T19:19:56.624952image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q319
95-th percentile36
Maximum51
Range51
Interquartile range (IQR)19

Descriptive statistics

Standard deviation13.665229
Coefficient of variation (CV)1.7021345
Kurtosis-0.0077367371
Mean8.0282898
Median Absolute Deviation (MAD)0
Skewness1.2803362
Sum18730
Variance186.73848
MonotonicityNot monotonic
2023-03-01T19:19:56.782531image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0 1695
72.7%
31 54
 
2.3%
30 34
 
1.5%
28 33
 
1.4%
24 32
 
1.4%
33 31
 
1.3%
27 30
 
1.3%
29 30
 
1.3%
26 27
 
1.2%
35 26
 
1.1%
Other values (35) 341
 
14.6%
ValueCountFrequency (%)
0 1695
72.7%
4 1
 
< 0.1%
8 2
 
0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
12 3
 
0.1%
13 1
 
< 0.1%
14 5
 
0.2%
15 7
 
0.3%
16 10
 
0.4%
ValueCountFrequency (%)
51 1
 
< 0.1%
50 1
 
< 0.1%
49 1
 
< 0.1%
48 2
 
0.1%
47 1
 
< 0.1%
46 3
 
0.1%
45 5
0.2%
44 6
0.3%
43 8
0.3%
42 10
0.4%

total day minutes
Real number (ℝ)

Distinct1402
Distinct (%)60.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean179.65568
Minimum0
Maximum350.8
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size36.5 KiB
2023-03-01T19:19:56.938115image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile91.82
Q1143.4
median179.2
Q3216.3
95-th percentile269.74
Maximum350.8
Range350.8
Interquartile range (IQR)72.9

Descriptive statistics

Standard deviation54.546284
Coefficient of variation (CV)0.30361569
Kurtosis0.009237259
Mean179.65568
Median Absolute Deviation (MAD)36.4
Skewness-0.0090433835
Sum419136.7
Variance2975.2971
MonotonicityNot monotonic
2023-03-01T19:19:57.091704image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
174.5 7
 
0.3%
175.4 7
 
0.3%
194.8 6
 
0.3%
178.7 6
 
0.3%
184.5 6
 
0.3%
154 6
 
0.3%
146.3 6
 
0.3%
155.2 5
 
0.2%
133.3 5
 
0.2%
182.1 5
 
0.2%
Other values (1392) 2274
97.5%
ValueCountFrequency (%)
0 1
< 0.1%
2.6 1
< 0.1%
7.8 1
< 0.1%
7.9 1
< 0.1%
12.5 1
< 0.1%
18.9 1
< 0.1%
25.9 1
< 0.1%
27 1
< 0.1%
29.9 1
< 0.1%
30.9 1
< 0.1%
ValueCountFrequency (%)
350.8 1
< 0.1%
346.8 1
< 0.1%
337.4 1
< 0.1%
334.3 1
< 0.1%
329.8 1
< 0.1%
328.1 1
< 0.1%
326.5 1
< 0.1%
326.3 1
< 0.1%
324.7 1
< 0.1%
322.5 1
< 0.1%

total day calls
Real number (ℝ)

Distinct115
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.56708
Minimum0
Maximum165
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size36.5 KiB
2023-03-01T19:19:57.258274image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q187
median101
Q3114
95-th percentile133
Maximum165
Range165
Interquartile range (IQR)27

Descriptive statistics

Standard deviation20.202414
Coefficient of variation (CV)0.20088496
Kurtosis0.15681189
Mean100.56708
Median Absolute Deviation (MAD)14
Skewness-0.096612563
Sum234623
Variance408.13754
MonotonicityNot monotonic
2023-03-01T19:19:57.412861image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
108 55
 
2.4%
102 53
 
2.3%
107 53
 
2.3%
110 50
 
2.1%
88 49
 
2.1%
101 48
 
2.1%
105 48
 
2.1%
91 46
 
2.0%
100 46
 
2.0%
95 45
 
1.9%
Other values (105) 1840
78.9%
ValueCountFrequency (%)
0 1
 
< 0.1%
35 1
 
< 0.1%
36 1
 
< 0.1%
40 2
0.1%
42 2
0.1%
44 2
0.1%
45 3
0.1%
47 2
0.1%
48 3
0.1%
49 2
0.1%
ValueCountFrequency (%)
165 1
 
< 0.1%
163 1
 
< 0.1%
160 1
 
< 0.1%
158 2
 
0.1%
152 1
 
< 0.1%
151 5
0.2%
150 4
0.2%
148 5
0.2%
147 4
0.2%
146 4
0.2%

total day charge
Real number (ℝ)

Distinct1402
Distinct (%)60.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.542015
Minimum0
Maximum59.64
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size36.5 KiB
2023-03-01T19:19:57.719057image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.608
Q124.38
median30.46
Q336.77
95-th percentile45.858
Maximum59.64
Range59.64
Interquartile range (IQR)12.39

Descriptive statistics

Standard deviation9.2728474
Coefficient of variation (CV)0.30360955
Kurtosis0.0094131402
Mean30.542015
Median Absolute Deviation (MAD)6.19
Skewness-0.0090423646
Sum71254.52
Variance85.985699
MonotonicityNot monotonic
2023-03-01T19:19:57.863670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.67 7
 
0.3%
29.82 7
 
0.3%
33.12 6
 
0.3%
30.38 6
 
0.3%
31.37 6
 
0.3%
26.18 6
 
0.3%
24.87 6
 
0.3%
26.38 5
 
0.2%
22.66 5
 
0.2%
30.96 5
 
0.2%
Other values (1392) 2274
97.5%
ValueCountFrequency (%)
0 1
< 0.1%
0.44 1
< 0.1%
1.33 1
< 0.1%
1.34 1
< 0.1%
2.13 1
< 0.1%
3.21 1
< 0.1%
4.4 1
< 0.1%
4.59 1
< 0.1%
5.08 1
< 0.1%
5.25 1
< 0.1%
ValueCountFrequency (%)
59.64 1
< 0.1%
58.96 1
< 0.1%
57.36 1
< 0.1%
56.83 1
< 0.1%
56.07 1
< 0.1%
55.78 1
< 0.1%
55.51 1
< 0.1%
55.47 1
< 0.1%
55.2 1
< 0.1%
54.83 1
< 0.1%

total eve minutes
Real number (ℝ)

Distinct1337
Distinct (%)57.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201.17578
Minimum0
Maximum354.2
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size36.5 KiB
2023-03-01T19:19:58.021249image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile119.96
Q1167.3
median202.4
Q3236
95-th percentile281.18
Maximum354.2
Range354.2
Interquartile range (IQR)68.7

Descriptive statistics

Standard deviation50.449386
Coefficient of variation (CV)0.25077266
Kurtosis0.090470194
Mean201.17578
Median Absolute Deviation (MAD)34.6
Skewness-0.063105629
Sum469343.1
Variance2545.1405
MonotonicityNot monotonic
2023-03-01T19:19:58.174838image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
169.9 8
 
0.3%
187 6
 
0.3%
220.6 6
 
0.3%
195.5 6
 
0.3%
201 6
 
0.3%
241.4 5
 
0.2%
226.7 5
 
0.2%
256.1 5
 
0.2%
230.9 5
 
0.2%
167.6 5
 
0.2%
Other values (1327) 2276
97.6%
ValueCountFrequency (%)
0 1
< 0.1%
31.2 1
< 0.1%
42.2 1
< 0.1%
42.5 1
< 0.1%
43.9 1
< 0.1%
49.2 1
< 0.1%
56 1
< 0.1%
58.6 1
< 0.1%
58.9 1
< 0.1%
60.8 1
< 0.1%
ValueCountFrequency (%)
354.2 1
< 0.1%
351.6 1
< 0.1%
350.9 1
< 0.1%
350.5 1
< 0.1%
348.5 1
< 0.1%
347.3 1
< 0.1%
341.3 1
< 0.1%
339.9 1
< 0.1%
337.1 1
< 0.1%
336 1
< 0.1%

total eve calls
Real number (ℝ)

Distinct115
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.885555
Minimum0
Maximum168
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size36.5 KiB
2023-03-01T19:19:58.342390image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q187
median100
Q3113
95-th percentile132
Maximum168
Range168
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.788878
Coefficient of variation (CV)0.19811551
Kurtosis0.28008549
Mean99.885555
Median Absolute Deviation (MAD)13
Skewness-0.067021385
Sum233033
Variance391.59968
MonotonicityNot monotonic
2023-03-01T19:19:58.490993image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105 57
 
2.4%
94 55
 
2.4%
108 52
 
2.2%
102 52
 
2.2%
109 50
 
2.1%
97 50
 
2.1%
88 50
 
2.1%
104 50
 
2.1%
101 47
 
2.0%
93 47
 
2.0%
Other values (105) 1823
78.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
12 1
 
< 0.1%
36 1
 
< 0.1%
42 1
 
< 0.1%
46 3
0.1%
48 2
0.1%
50 3
0.1%
51 4
0.2%
52 4
0.2%
53 2
0.1%
ValueCountFrequency (%)
168 1
 
< 0.1%
159 1
 
< 0.1%
157 1
 
< 0.1%
156 1
 
< 0.1%
155 2
0.1%
154 2
0.1%
153 1
 
< 0.1%
152 3
0.1%
151 1
 
< 0.1%
150 3
0.1%

total eve charge
Real number (ℝ)

Distinct1215
Distinct (%)52.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.10021
Minimum0
Maximum30.11
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size36.5 KiB
2023-03-01T19:19:58.655568image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.196
Q114.22
median17.2
Q320.06
95-th percentile23.898
Maximum30.11
Range30.11
Interquartile range (IQR)5.84

Descriptive statistics

Standard deviation4.2881942
Coefficient of variation (CV)0.25076851
Kurtosis0.090357286
Mean17.10021
Median Absolute Deviation (MAD)2.94
Skewness-0.06314914
Sum39894.79
Variance18.388609
MonotonicityNot monotonic
2023-03-01T19:19:58.810155image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.25 9
 
0.4%
15.9 9
 
0.4%
16.12 8
 
0.3%
14.44 8
 
0.3%
17.09 8
 
0.3%
18.79 7
 
0.3%
18.16 7
 
0.3%
17.77 7
 
0.3%
18.67 6
 
0.3%
17.94 6
 
0.3%
Other values (1205) 2258
96.8%
ValueCountFrequency (%)
0 1
< 0.1%
2.65 1
< 0.1%
3.59 1
< 0.1%
3.61 1
< 0.1%
3.73 1
< 0.1%
4.18 1
< 0.1%
4.76 1
< 0.1%
4.98 1
< 0.1%
5.01 1
< 0.1%
5.17 1
< 0.1%
ValueCountFrequency (%)
30.11 1
< 0.1%
29.89 1
< 0.1%
29.83 1
< 0.1%
29.79 1
< 0.1%
29.62 1
< 0.1%
29.52 1
< 0.1%
29.01 1
< 0.1%
28.89 1
< 0.1%
28.65 1
< 0.1%
28.56 1
< 0.1%

total night minutes
Real number (ℝ)

Distinct1360
Distinct (%)58.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201.21174
Minimum23.2
Maximum377.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.5 KiB
2023-03-01T19:19:58.971723image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum23.2
5-th percentile117.18
Q1166.9
median201.6
Q3236.6
95-th percentile282.84
Maximum377.5
Range354.3
Interquartile range (IQR)69.7

Descriptive statistics

Standard deviation50.888058
Coefficient of variation (CV)0.25290799
Kurtosis0.02417188
Mean201.21174
Median Absolute Deviation (MAD)34.9
Skewness-0.017880491
Sum469427
Variance2589.5944
MonotonicityNot monotonic
2023-03-01T19:19:59.121323image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
192.7 6
 
0.3%
214.6 6
 
0.3%
193.6 6
 
0.3%
197.4 6
 
0.3%
214 6
 
0.3%
194.3 6
 
0.3%
191.4 5
 
0.2%
221.6 5
 
0.2%
223.5 5
 
0.2%
202 5
 
0.2%
Other values (1350) 2277
97.6%
ValueCountFrequency (%)
23.2 1
< 0.1%
43.7 1
< 0.1%
45 1
< 0.1%
50.1 1
< 0.1%
53.3 1
< 0.1%
54 1
< 0.1%
54.5 1
< 0.1%
56.6 1
< 0.1%
61.4 1
< 0.1%
63.3 1
< 0.1%
ValueCountFrequency (%)
377.5 1
< 0.1%
367.7 1
< 0.1%
364.9 1
< 0.1%
364.3 1
< 0.1%
352.5 1
< 0.1%
352.2 1
< 0.1%
349.7 1
< 0.1%
349.2 1
< 0.1%
345.8 1
< 0.1%
344.3 1
< 0.1%

total night calls
Real number (ℝ)

Distinct111
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.988856
Minimum33
Maximum164
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.5 KiB
2023-03-01T19:19:59.303869image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile68
Q187
median100
Q3113
95-th percentile132
Maximum164
Range131
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.406455
Coefficient of variation (CV)0.19408618
Kurtosis-0.15527869
Mean99.988856
Median Absolute Deviation (MAD)13
Skewness0.043855741
Sum233274
Variance376.61051
MonotonicityNot monotonic
2023-03-01T19:19:59.468435image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
104 60
 
2.6%
105 58
 
2.5%
91 54
 
2.3%
94 54
 
2.3%
100 52
 
2.2%
92 49
 
2.1%
102 48
 
2.1%
109 47
 
2.0%
106 47
 
2.0%
103 47
 
2.0%
Other values (101) 1817
77.9%
ValueCountFrequency (%)
33 1
 
< 0.1%
36 1
 
< 0.1%
48 1
 
< 0.1%
49 1
 
< 0.1%
50 1
 
< 0.1%
52 2
 
0.1%
53 4
0.2%
54 1
 
< 0.1%
55 5
0.2%
56 2
 
0.1%
ValueCountFrequency (%)
164 1
 
< 0.1%
157 2
0.1%
156 2
0.1%
155 1
 
< 0.1%
154 1
 
< 0.1%
153 1
 
< 0.1%
152 2
0.1%
151 3
0.1%
150 2
0.1%
149 1
 
< 0.1%

total night charge
Real number (ℝ)

Distinct852
Distinct (%)36.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0545907
Minimum1.04
Maximum16.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.5 KiB
2023-03-01T19:19:59.641929image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.04
5-th percentile5.276
Q17.51
median9.07
Q310.65
95-th percentile12.73
Maximum16.99
Range15.95
Interquartile range (IQR)3.14

Descriptive statistics

Standard deviation2.2900122
Coefficient of variation (CV)0.25291173
Kurtosis0.023898665
Mean9.0545907
Median Absolute Deviation (MAD)1.57
Skewness-0.017924807
Sum21124.36
Variance5.2441559
MonotonicityNot monotonic
2023-03-01T19:19:59.813471image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.66 10
 
0.4%
7.69 10
 
0.4%
9.32 10
 
0.4%
9.18 9
 
0.4%
8.47 9
 
0.4%
8.64 9
 
0.4%
9.63 9
 
0.4%
8.59 9
 
0.4%
9.09 9
 
0.4%
7.52 9
 
0.4%
Other values (842) 2240
96.0%
ValueCountFrequency (%)
1.04 1
< 0.1%
1.97 1
< 0.1%
2.03 1
< 0.1%
2.25 1
< 0.1%
2.4 1
< 0.1%
2.43 1
< 0.1%
2.45 1
< 0.1%
2.55 1
< 0.1%
2.76 1
< 0.1%
2.85 1
< 0.1%
ValueCountFrequency (%)
16.99 1
< 0.1%
16.55 1
< 0.1%
16.42 1
< 0.1%
16.39 1
< 0.1%
15.86 1
< 0.1%
15.85 1
< 0.1%
15.74 1
< 0.1%
15.71 1
< 0.1%
15.56 1
< 0.1%
15.49 1
< 0.1%

total intl minutes
Real number (ℝ)

Distinct154
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.269567
Minimum0
Maximum20
Zeros9
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size36.5 KiB
2023-03-01T19:20:00.023909image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.7
Q18.5
median10.4
Q312.1
95-th percentile14.7
Maximum20
Range20
Interquartile range (IQR)3.6

Descriptive statistics

Standard deviation2.7776012
Coefficient of variation (CV)0.27046916
Kurtosis0.46548857
Mean10.269567
Median Absolute Deviation (MAD)1.8
Skewness-0.2061847
Sum23958.9
Variance7.7150683
MonotonicityNot monotonic
2023-03-01T19:20:00.204426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 42
 
1.8%
11.1 41
 
1.8%
10 41
 
1.8%
11.3 40
 
1.7%
9.7 40
 
1.7%
10.5 39
 
1.7%
10.1 39
 
1.7%
11.5 38
 
1.6%
10.6 38
 
1.6%
9.8 38
 
1.6%
Other values (144) 1937
83.0%
ValueCountFrequency (%)
0 9
0.4%
1.3 1
 
< 0.1%
2 2
 
0.1%
2.1 2
 
0.1%
2.4 1
 
< 0.1%
2.5 1
 
< 0.1%
2.6 1
 
< 0.1%
2.7 1
 
< 0.1%
2.9 2
 
0.1%
3.3 2
 
0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
18.4 1
 
< 0.1%
18.2 2
0.1%
18 2
0.1%
17.8 2
0.1%
17.6 2
0.1%
17.5 3
0.1%
17.3 1
 
< 0.1%
17.1 1
 
< 0.1%
17 2
0.1%

total intl calls
Real number (ℝ)

Distinct21
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5032147
Minimum0
Maximum20
Zeros9
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size36.5 KiB
2023-03-01T19:20:00.364998image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q36
95-th percentile9
Maximum20
Range20
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.5075551
Coefficient of variation (CV)0.55683668
Kurtosis3.5281097
Mean4.5032147
Median Absolute Deviation (MAD)1
Skewness1.3994611
Sum10506
Variance6.2878327
MonotonicityNot monotonic
2023-03-01T19:20:00.491658image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
3 461
19.8%
4 424
18.2%
2 354
15.2%
5 319
13.7%
6 235
10.1%
7 168
 
7.2%
1 114
 
4.9%
8 84
 
3.6%
9 75
 
3.2%
10 32
 
1.4%
Other values (11) 67
 
2.9%
ValueCountFrequency (%)
0 9
 
0.4%
1 114
 
4.9%
2 354
15.2%
3 461
19.8%
4 424
18.2%
5 319
13.7%
6 235
10.1%
7 168
 
7.2%
8 84
 
3.6%
9 75
 
3.2%
ValueCountFrequency (%)
20 1
 
< 0.1%
19 1
 
< 0.1%
18 3
 
0.1%
17 1
 
< 0.1%
16 2
 
0.1%
15 6
 
0.3%
14 4
 
0.2%
13 9
0.4%
12 11
0.5%
11 20
0.9%

total intl charge
Real number (ℝ)

Distinct154
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7733648
Minimum0
Maximum5.4
Zeros9
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size36.5 KiB
2023-03-01T19:20:00.649237image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.54
Q12.3
median2.81
Q33.27
95-th percentile3.97
Maximum5.4
Range5.4
Interquartile range (IQR)0.97

Descriptive statistics

Standard deviation0.74992947
Coefficient of variation (CV)0.27040419
Kurtosis0.46643626
Mean2.7733648
Median Absolute Deviation (MAD)0.48
Skewness-0.20624876
Sum6470.26
Variance0.56239421
MonotonicityNot monotonic
2023-03-01T19:20:00.814794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.97 42
 
1.8%
3 41
 
1.8%
2.7 41
 
1.8%
3.05 40
 
1.7%
2.62 40
 
1.7%
2.84 39
 
1.7%
2.73 39
 
1.7%
3.11 38
 
1.6%
2.86 38
 
1.6%
2.65 38
 
1.6%
Other values (144) 1937
83.0%
ValueCountFrequency (%)
0 9
0.4%
0.35 1
 
< 0.1%
0.54 2
 
0.1%
0.57 2
 
0.1%
0.65 1
 
< 0.1%
0.68 1
 
< 0.1%
0.7 1
 
< 0.1%
0.73 1
 
< 0.1%
0.78 2
 
0.1%
0.89 2
 
0.1%
ValueCountFrequency (%)
5.4 1
 
< 0.1%
4.97 1
 
< 0.1%
4.91 2
0.1%
4.86 2
0.1%
4.81 2
0.1%
4.75 2
0.1%
4.73 3
0.1%
4.67 1
 
< 0.1%
4.62 1
 
< 0.1%
4.59 2
0.1%

customer service calls
Real number (ℝ)

Distinct10
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5516502
Minimum0
Maximum9
Zeros508
Zeros (%)21.8%
Negative0
Negative (%)0.0%
Memory size36.5 KiB
2023-03-01T19:20:01.091055image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3287018
Coefficient of variation (CV)0.85631531
Kurtosis1.5878123
Mean1.5516502
Median Absolute Deviation (MAD)1
Skewness1.0853073
Sum3620
Variance1.7654486
MonotonicityNot monotonic
2023-03-01T19:20:01.181813image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 823
35.3%
2 513
22.0%
0 508
21.8%
3 294
 
12.6%
4 125
 
5.4%
5 43
 
1.8%
6 19
 
0.8%
7 5
 
0.2%
8 2
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
0 508
21.8%
1 823
35.3%
2 513
22.0%
3 294
 
12.6%
4 125
 
5.4%
5 43
 
1.8%
6 19
 
0.8%
7 5
 
0.2%
8 2
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
8 2
 
0.1%
7 5
 
0.2%
6 19
 
0.8%
5 43
 
1.8%
4 125
 
5.4%
3 294
 
12.6%
2 513
22.0%
1 823
35.3%
0 508
21.8%

churn
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.5 KiB
False
1984 
True
349 
ValueCountFrequency (%)
False 1984
85.0%
True 349
 
15.0%
2023-03-01T19:20:01.288528image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Interactions

2023-03-01T19:19:52.943807image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:15.891101image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:18.048340image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:20.574851image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:22.866188image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:24.931782image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:27.106526image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:29.250121image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:31.492863image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:35.283573image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:38.873040image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:42.226078image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:44.770279image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:47.255037image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:50.594081image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:53.073492image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:16.021751image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:18.173283image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:20.721135image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:23.003536image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:25.058469image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:27.238198image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:29.380700image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:31.618527image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:35.458109image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:39.124370image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:42.461469image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:44.907926image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:47.474420image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:50.767617image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:53.214115image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:16.136445image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:18.317863image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:20.853783image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:23.123504image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:25.185613image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:27.377443image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:29.559369image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:31.752170image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:35.628652image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:39.359769image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:42.643960image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:45.044561image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:47.681894image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:50.935168image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:53.350793image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:16.265107image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:18.541266image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:20.994406image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:23.261776image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:25.319502image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:27.535365image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:29.770727image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:31.918571image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:35.806178image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:39.594115image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:42.828467image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:45.189173image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:47.898287image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:51.103717image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:53.479406image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:16.400745image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:18.711812image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:21.133697image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:23.385905image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:25.443390image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:27.673781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:29.934532image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:32.099089image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:35.993677image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:39.825493image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:43.013974image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:45.327803image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:48.087781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:51.267280image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:53.607066image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:16.553337image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:18.964137image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:21.348379image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:23.514491image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:25.569811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:27.816455image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:30.074371image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:32.352411image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:36.160232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:40.078840image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:43.189506image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:45.469425image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:48.278271image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:51.420870image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:53.739710image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:16.691966image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:19.139668image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:21.546245image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:23.656345image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:25.708238image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:27.963489image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:30.237493image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:32.642634image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:36.318806image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:40.306233image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:43.360048image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:45.619023image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:48.453803image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:51.571468image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:53.868367image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:16.823613image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:19.288269image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:21.720295image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:23.806326image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:25.976624image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:28.112334image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:30.377183image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:32.859058image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:36.491346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:40.497701image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:43.516630image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:45.765633image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:48.622352image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:51.712224image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:53.993033image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:16.954265image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:19.455822image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:21.895475image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:23.958210image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:26.101770image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:28.250966image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:30.516282image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:33.535248image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:36.728915image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:40.682206image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:43.664233image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:45.913237image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:48.853733image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:51.848896image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:54.118697image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:17.086910image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:19.602431image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:22.055322image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:24.097403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:26.237412image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:28.392486image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:30.636960image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:33.883320image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:36.961291image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:40.928550image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:43.808849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:46.062404image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:49.061178image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:51.999457image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:54.247353image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:17.222547image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:19.758013image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:22.183783image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:24.228688image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:26.369689image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:28.541417image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:30.796534image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:34.148611image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:37.233563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:41.127038image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:43.952464image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:46.207018image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:49.307520image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:52.150054image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:54.392964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:17.347214image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:19.925567image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:22.317743image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:24.378595image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:26.508674image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:28.686354image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:30.945137image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:34.445814image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:37.601581image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:41.361391image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:44.090095image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:46.371369image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:49.610710image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:52.290684image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:54.536580image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:17.523741image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:20.098122image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:22.467223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:24.531498image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:26.657059image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:28.837273image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:31.095734image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:34.693156image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:37.940672image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:41.602746image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:44.381318image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:46.573828image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:49.854061image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:52.574793image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:54.677204image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:17.747144image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:20.263678image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:22.619426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:24.678580image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:26.827415image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:28.985531image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:31.252504image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:34.921543image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:38.217368image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:41.820166image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:44.522940image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:46.807203image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:50.113366image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:52.710466image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:54.798919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:17.928658image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:20.446196image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:22.738675image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:24.809632image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:26.981099image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:29.115365image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:31.373208image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:35.118016image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:38.558455image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:42.015644image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:44.648604image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:47.035594image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:50.374667image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-01T19:19:52.828117image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-03-01T19:20:01.398234image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
account lengthnumber vmail messagestotal day minutestotal day callstotal day chargetotal eve minutestotal eve callstotal eve chargetotal night minutestotal night callstotal night chargetotal intl minutestotal intl callstotal intl chargecustomer service callsstatearea codeinternational planvoice mail planchurn
account length1.000-0.0090.0160.0340.016-0.0100.005-0.010-0.025-0.015-0.0250.0190.0170.019-0.0020.0280.0290.0000.0000.000
number vmail messages-0.0091.000-0.008-0.001-0.008-0.002-0.002-0.002-0.0160.006-0.0160.005-0.0020.005-0.0050.0000.0000.0410.9970.094
total day minutes0.016-0.0081.000-0.0061.0000.0210.0380.0210.0010.0240.001-0.007-0.004-0.007-0.0200.0230.0380.0510.0500.362
total day calls0.034-0.001-0.0061.000-0.006-0.0050.012-0.0050.027-0.0030.027-0.002-0.000-0.002-0.0270.0190.0000.0000.0000.067
total day charge0.016-0.0081.000-0.0061.0000.0210.0380.0210.0010.0240.001-0.007-0.004-0.007-0.0200.0240.0380.0490.0490.362
total eve minutes-0.010-0.0020.021-0.0050.0211.000-0.0011.000-0.0220.011-0.0220.0020.0070.002-0.0290.0000.0200.0290.0000.079
total eve calls0.005-0.0020.0380.0120.038-0.0011.000-0.0010.011-0.0010.011-0.0020.024-0.0020.0140.0000.0000.0000.0000.000
total eve charge-0.010-0.0020.021-0.0050.0211.000-0.0011.000-0.0220.011-0.0220.0020.0070.002-0.0290.0000.0200.0290.0000.079
total night minutes-0.025-0.0160.0010.0270.001-0.0220.011-0.0221.0000.0061.000-0.017-0.007-0.017-0.0120.0060.0000.0000.0000.000
total night calls-0.0150.0060.024-0.0030.0240.011-0.0010.0110.0061.0000.006-0.0100.004-0.010-0.0080.0320.0200.0000.0000.000
total night charge-0.025-0.0160.0010.0270.001-0.0220.011-0.0221.0000.0061.000-0.017-0.007-0.017-0.0120.0000.0000.0000.0000.000
total intl minutes0.0190.005-0.007-0.002-0.0070.002-0.0020.002-0.017-0.010-0.0171.0000.0011.000-0.0090.0000.0000.0000.0000.042
total intl calls0.017-0.002-0.004-0.000-0.0040.0070.0240.007-0.0070.004-0.0070.0011.0000.0010.0000.0000.0000.0190.0000.095
total intl charge0.0190.005-0.007-0.002-0.0070.002-0.0020.002-0.017-0.010-0.0171.0000.0011.000-0.0090.0000.0000.0000.0000.042
customer service calls-0.002-0.005-0.020-0.027-0.020-0.0290.014-0.029-0.012-0.008-0.012-0.0090.000-0.0091.0000.0170.0000.0730.0170.317
state0.0280.0000.0230.0190.0240.0000.0000.0000.0060.0320.0000.0000.0000.0000.0171.0000.0000.0630.0000.088
area code0.0290.0000.0380.0000.0380.0200.0000.0200.0000.0200.0000.0000.0000.0000.0000.0001.0000.0470.0000.000
international plan0.0000.0410.0510.0000.0490.0290.0000.0290.0000.0000.0000.0000.0190.0000.0730.0630.0471.0000.0000.228
voice mail plan0.0000.9970.0500.0000.0490.0000.0000.0000.0000.0000.0000.0000.0000.0000.0170.0000.0000.0001.0000.089
churn0.0000.0940.3620.0670.3620.0790.0000.0790.0000.0000.0000.0420.0950.0420.3170.0880.0000.2280.0891.000

Missing values

2023-03-01T19:19:55.000340image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-01T19:19:55.398316image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

stateaccount lengtharea codephone numberinternational planvoice mail plannumber vmail messagestotal day minutestotal day callstotal day chargetotal eve minutestotal eve callstotal eve chargetotal night minutestotal night callstotal night chargetotal intl minutestotal intl callstotal intl chargecustomer service callschurn
1402NE70415421-8535nono0213.48636.28204.77717.40256.610111.555.741.541False
1855WI67510417-2265nono0109.113418.55142.37612.1091.2864.1010.952.942False
633NJ122415327-9341noyes34146.410424.8989.71037.62220.0919.9015.644.212False
1483NV107510419-9688yesno0234.19139.80163.110513.86282.510012.7110.032.701False
2638HI105510364-8128nono0125.411621.32261.59522.23241.610410.8711.493.082False
733NM85408338-9210noyes37229.612339.03132.39011.25211.9769.549.582.572False
28MO20415353-2630nono0190.010932.30258.28421.95181.51028.176.361.700False
110MI120408409-3786nono0165.010028.05317.28326.96119.2865.368.382.241False
2028SD93510408-4836nono0328.110655.78151.78912.89303.511413.668.732.351True
2670WY116510392-2733noyes12221.010837.57151.011812.84179.0808.069.062.432False
stateaccount lengtharea codephone numberinternational planvoice mail plannumber vmail messagestotal day minutestotal day callstotal day chargetotal eve minutestotal eve callstotal eve chargetotal night minutestotal night callstotal night chargetotal intl minutestotal intl callstotal intl chargecustomer service callschurn
96MT73415370-3450nono0160.111027.22213.37218.13174.1727.8313.043.510False
1761TN127415339-7684noyes28235.612440.05236.811320.13241.212710.857.722.081False
3286OH106415352-2270noyes30220.110537.42222.210918.89158.4967.1313.183.540False
1593KS105415405-1108yesno0273.911946.56278.610323.68255.39011.4910.972.941True
1147MN95408340-4627noyes32262.212344.57165.28214.04194.3578.7410.652.860False
2154WY126408339-9798yesno0197.612633.59246.511220.95285.310412.8412.583.382False
3089WV70510348-3777noyes30143.47224.38170.09214.45127.9685.769.442.543False
1766NJ125415406-6400nono0182.36430.99139.812111.88171.6967.7211.673.132False
1122NE159415362-5111nono0189.110532.15246.114720.92242.010610.8910.452.811True
1346PA106408403-9167yesno0133.74522.73187.810715.96181.9898.1910.722.891True